Architecture: How to supercharge mcp-gograb-ai to reach 100/100 score?

Documentation for Sessions

🏗️ Architecture: How to supercharge mcp-gograb-ai to reach 100/100 score?

📋 Decision Context

Question:

How to supercharge mcp-gograb-ai to reach 100/100 score?

Task: Session 42: Supercharging mcp-gograb-ai with Tavily, Knowledge Graph, and Playwright

🎯 Decision Made

Chosen:

Hybrid: Integrated Tavily web search and Knowledge Graph directly into mcp-gograb-ai, plus use Playwright as companion MCP

Options Considered:

Add external MCPs only (Tavily, Puppeteer)
Integrate features directly into mcp-gograb-ai
Hybrid: integrate critical features + use companion MCPs

💭 Reasoning

Integrating Tavily and Knowledge Graph directly gives us control over caching, learning from searches, and linking graph entities to decisions. Playwright is kept separate because it's already installed and working well as a companion MCP. This hybrid approach maximizes value while minimizing complexity.

📁 Files Involved

MCP/mcp-development-ai/src/tools/web-search.ts
MCP/mcp-development-ai/src/tools/knowledge-graph.ts
MCP/mcp-development-ai/src/server.ts
.cursor/mcp-config.json

🔗 Related

Decision ID: dev_1768409713644_mumkbvf
Session: 42
Workspace: editor2

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